Visualization and analysis of large three dimensional (3D) Computed Tomography Angiography (CTA) image data has become a conventional practice. The visualization of data is commonly achieved by a maximum intensity projection or by volume rendering. However, due to the overlapping intensity distribution between bone and contrast enhanced vessels in CTA data, bone structures can be a major obstacle in the visualization and analysis of vessel trees, aneurisms and calcifications.
In the past, manual editing techniques have been used to extract and remove bone structures from the data. However, the tedious and long operating time required for the manual editing is prohibitive for it to be of practical use. This is particularly true as the size of the acquired data increases. For example, 3D images of the abdomen and legs are acquired today in 1000-2000 traverse images with 512×512 12-bit pixels for analysis of peripheral arterial occlusion diseases.
Semi-automatic methods exist for segmenting bone structures from a focused region of the body; however, these methods are often inefficient for use with large data, or are not sufficiently robust for extension to automatic clinical use cases. For example, methods that are crucially based on specific industry threshold constraints are generally not robust foundations for segmentation due to the variability of intensities in the different organ parts of a large image and in different data. Region growing based and other more elaborate graph based methods also pose problems for speed and memory consumption efficiency due to either incoherent data memory access or large memory consumption. These approaches are usually used for data capturing only a small region of the body, or for subsampled, lower resolution, versions of the original data.
Region growing based methods that rely only on region connectivity and simple constraints such as threshold ranges, also often face challenges when bones and vessels with overlapping intensity distribution also appear to be connected due to image resolution and noise in the data, which is a common case in routine clinical data. Other known methods had tried to improve the robustness by incorporating a priori knowledge. However, even when an anatomical atlas is available as the basis of a priori knowledge, the method still requires lengthy registration and sometimes manual interventions that are still not yet practical for routine use.